Performance of Zero-Shot Cross-Lingual Retrieval Models on Artificially Code-Switched Data vs. Multilingual Datasets in

Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-reso Research goal: How does the performance of zero-shot cross-lingual retrieval models trained on artificially code-switched data compare to models fine-tuned on multilingual datasets like mC4 or OSCAR across a broader range of low-resource languages in XNLI? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.3/10.